Comparison of Matched Patient Data for SSIs following Total Hip and Total Knee Arthoplasty: IPC Versus NSQIP Surveillance
Bibliographic record
Abstract
Background: In Alberta, Canada, surgical site infections (SSIs) following total hip and knee replacements (THRs and TKRs) are reported using the infection prevention and control (IPC) surveillance system, which surveys all THRs and TKRs using the NHSN definitions; and the National Surgical Quality Improvement Program (NSQIP), which uses different definitions and sampling strategies. Deterministic matching of patient data from these sources was used to examine the overlap and discrepancies in SSI reporting. Methods: A retrospective multisite cohort study of IPC and NSQIP superficial, deep, and organ-space THR/TKR SSI data collected 30 days postoperatively from September 1, 2015, to March 31, 2018 was undertaken. To identify patients with procedures captured by both IPC and NSQIP, data were cleaned, duplicates removed, and patients matched 1:1 using year of birth, procedure facility, type, side, date, and time. Positive and negative agreement were assessed, and the Cohen κ values were calculated. The definitions and data capture methods used by both IPC and NSQIP were also compared. Results: There were 7,549 IPC and 2,037 NSQIP patients, respectively, with 1,798 matched patients: IPC (23.8%) and NSQIP (88.3%). Moreover, 17 SSIs were identified by both IPC and NSQIP, including 9 superficial and 8 complex by IPC and 6 superficial and 11 complex by NSQIP. Also, 7 SSIs were identified only by IPC, of which 5 were superficial, and 36 SSIs were identified only by NSQIP, of which 28 were superficial (positive agreement, 0.44; negative agreement, 0.99; κ = .43). Excluding superficial SSIs, 7 SSIs were identified by both IPC and NSQIP; 3 were identified only by IPC; and 12 were identified only by NSQIP (positive agreement, 0.48; negative agreement, 1.00; κ = 0.48). Conclusions: THR/TKR SSI rates reported by IPC and NSQIP were not comparable in this matched dataset. NSQIP identifies more superficial SSIs. Variations in data capture methods and definitions accounted for most of the discordance. Both surveillance systems are critically involved with improving patient outcomes following surgery. However, stakeholders need to be aware of these variations, and education should be provided to facilitate an understanding of the differences and their interpretation. Future work should explore other surgical procedures and larger data sets. Funding: None Disclosures: None
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".